This invention relates in general to data and information storage and retrieval, and more specifically to capture, storage, machine learning based tagging, scoring, ranking, discovery, filtering and transmission of data objects representing content items, for example, content items captured as segmented media.
Organizations generate large amount of data and content based on user interactions. Various types of content generated by users is spread across various applications and formats. For example, content may be provided and exchanged via emails, videos, documents, web pages, or shared on calls as conversations, and so on. However, significant content often gets lost within the large amount of information generated. For example, a user may draft a document describing a concept that is significant for the enterprise. However, conventional techniques for information retrieval may not rank the document very high in enterprise searches. As a result, the document is likely to get lost within other documents generated by other users. This causes several significant content items provided by users to get lost in the plethora of the information generated by the enterprise without giving a chance to other users to view or comment upon the content item. Conventional techniques rely on the manual efforts of the user in delivering the content item to other users in order to disseminate the information captured within the content item. Accordingly content items aggressively promoted by individuals gain higher visibility with the enterprise even if the content items are not significant with respect to particular topics of interest to users of the enterprise.